Data mining methods in medicine is a very important tool for developing automated decision support systems. However, since information granularity of disease codes used in hospital information system is coarser than that of real clinical definitions of diseases and their treatment, automated data curation is needed to extract knowledge useful for clinical decision making. This paper proposes automated construction of clinical process plan from nursing order histories and discharge summaries stored in hospital information system with curation of disease codes as follows. First, the system applies EM clustering to estimate subgrouping of a given disease code from clinical cases. Second, it decomposes the original datasets into datasets of subgroups by using granular homogenization. Thirdly, clinical pathway generation method is applied to the datasets. Fourthly, classification models of subgroups are constructed by using the analysis of discharge summaries to capture the meaning of each subgroup. Finally, the clinical pathway of a given disease code is output as the combination of the classifiers of subgroups and the the pathways of the corresponding subgroups. The proposed method was evaluated on the datasets extracted hospital information system in Shimane University Hosptial. The obtained results show that more plausible clinical pathways were obtained, compared with previously introduced methods.

This paper proposes a method which classifies discharge summaries stored in hospital information system, which consists of the following four steps. First, a term matrix of the set of summaries is induced by morphological analysis (RMecab). Next, correspondence analysis is applied to the term matrix and numerical values of two dimensional coordinates are assigned to each keyword and each concept. By measuring the euclidean distance between categories and keywords, keywords are ordered. Then, keywords are selected as attributes according to the rank, and training examples for classifiers will be generated. Finally, learning methods are applied to the training examples. Experimental validation shows that random forest achieved the best performance and deep learning (multiple layer perceptron) is the second best.

This paper proposes construction of clinical process plan from nursing order histories and discharge summaries stored in hospital information system. First, the system extracts subgrouping from clinical cases with the same Diagnostic Procedure Combination code (DPC) by mixture model clustering. Subgroups give different types of diseases with different temporal evolution. Then, classification models of each subgroup are constructed by the analysis of discharge summaries to capture the meaning of each subgroup. Finally, cases are classified by using the classification model and a clinical pathway is generated for each new subgroup. The proposed method was evaluated on the datasets extracted hospital information system, whose results show that plausible clinical pathways were obtained, compared with previously introduced methods.

This paper proposes the following two-fold clinical care generation method. First, the system proposes how clinical cases with the same Diagnostic Procedure Combination code (DPC) are characterized by mixture model clustering, and constructs classification model by the analysis of discharge summaries. Then, cases are classified by using the classification model and a clinical pathway is generated for each new class. The proposed method was evaluated on the datasets extracted hospital information system, whose results show that plausible clinical pathways were obtained, compared with previously introduced methods.

This paper proposes a method for empirical comparison of distances for agglomerative hierarchical clustering based on rough set-based approximation. When a set of target is given, a level of clustering tree where one branch includes all the targets can be traced with the number of elements included. The pair (#clusters of a level, #elements of a cluster) can be viewed as indices-pair for a given clustering tree.

Electronic patient records (EPR) are rich in texts, where almost all the decision making processes of medical staff are written. Thus, mining in EPR is important for acquision of decision making process and diagnosis. In this paper, as a first step, we focus on text mining for discharge summaries, which include the compact explanation for the patient's admission. a record of her complaints, physical findings, laboratory results and radiographic studies while hospitalized; a list of changes in her medications at discharge; and recommendations for follow up care. Text mining process consists of the following four processes: first, morphological analysis is applied to a set of summaries and a term matrix is generated. Second, correspond analysis is applied to the classification labels and the term matrix and generates two dimensional coordinates. By measuring the distances between categories and the assigned points, ranking of key words will be generated. Then, keywords are selected as attributes according to the rank, and training examples for classifiers will be generated. Finally, learning methods are applied to the training examples. Experimental validation shows that random forest achieved the best performance and the second best was the deep learner with a small difference, but decision tree methods with many keywords performed only a little worse than neural network or deep learning methods.

This paper proposes a method which induces a clinical pathway by using sample and attribute clustering of the histories of nursing orders stored in hospital information system. The method consists of the following five steps: first, frequencies of nursing orders are extracted from hospital information system. Second, orders are classified into several groups by using sample clustering. Then, attributes clustering is applied to the data for feature selection. Fourth, the method compares between generated functions for sample and attribute clustering which relate the number of clusters and calculated similarities. Fifth, if attribute clustering gives better performance with respect to the function, the dataset is decomposed into subtables by using the grouping of attribute clustering. Then, the first step will be repeated in a recursive way. After the grouping results are stable, a new pathway will be constructed from all the induced results. The method was applied to datasets of a disease extracted from a hospital information system. The results show that the proposed method is useful for construction of a clinical pathway.

This paper proposes an active mining process for improvement of quality of clinical process by using service logs in a hospital information system. First, datasets of temporal change of the number of orders are extracted from service logs stored in hospital information system. Then, since datasets of temporal change can be viewed as time-series of a statistic, clustering can be applied to the data. By using the groups obtained, datasets of command sequences are extracted from the logs and sequence mining process is applied. The results of sequence mining are interpreted with the results of clustering and hypothesis will be generated. The results show that the method improved the clinical process and waiting time in outpatient clinic.

This paper proposes a method for construction of a clinical pathway based on attribute and sample clustering, called dual clustering. The method consists of the following five steps: first, histories of nursing orders are extracted from hospital information system. Second, orders are classified into several groups by using clustering on the pricipal components (sample clustering). Third, attributes clustering is applied to the data. Fourth, the method compares between generated functions for sample and attribute clustering which relate the number of clusters and calculated similarities. Fifth, if attribute clustering gives better performance with respect to the function, the dataset is decomposed into subtables by using the grouping of attribute clustering. Then, the first step will be repeated in a recursive way. After the grouping results are stable, a new pathway will be constructed from all the induced results. The method was applied to datasets of a disease extracted from a hospital information system. The results show that the proposed method is useful for construction of a clinical pathway.

[Introduction] Schedule management of hospitalization is important to maintain or improve the quality of medical care and application of a clinical pathway is one of the important solutions for the management. Although several kinds of deductive methods for construction for a clinical pathway have been proposed, the customization is one of the important problems. This research proposed an inductive approach to support the customization of existing clinical pathways by using data on nursing actions stored in a hospital information system. [Method] The number of each nursing action applied to a given disease during the hospitalization was counted for each day as a temporal sequence. Temporal sequences were compared by using clustering and multidimensional scaling method in order to visualize the similarities between temporal patterns of clinical actions. [Results] Clustering and multidimensional scaling analysis classified these orders to one group necessary for the treatment for this DPC and the other specific to the status of a patient. The method was evaluated on data sets of ten frequent diseases extracted from hospital information system in Shimane University Hospital. Cataracta and Glaucoma were selected. Removing routine and poorly documented nursing actions, 46 items were selected for analysis. [Discussion] Counting data on executed nursing orders were analyzed as temporal sequences by using similarity-based analysis methods. The analysis classified the nursing actions into the two major groups: one consisted of orders necessary for the treatment and the other consisted of orders dependent on the status of admitted patients, including complicated diseases, such as DM or heart diseases. The method enabled us to inductive construction of standardized schedule management and detection of the conditions of patients difficult to apply the existing or induced clinical pathway.

Schedule management of hospitalization is important to maintain or improve the quality of medical care. Application of a clinical pathway has been proposed as one of the important solutions for the management. This research proposed an data-oriented maintenance and construction of clinical pathways by using data on histories of nursing orders stored in hospital information system. The method was evaluated on data extracted from a hospital information system. The results show that the reuse of stored data will give a powerful tool for management of nursing schedule and lead to improvement of hospital services.

This paper proposes granularity-based temporal data mining method which constructs clinical process conducted by nurses. The methods consist of three process. First, data on counting sum of executed orders are extracted from hospital informaton system with a given temporal granularity. Then, similarity-based methods, such as clustering and multidimensional scaling (MDS) are applied to the data and the labels for grouping are obtained. By using the labels, rule induction is applied, and classification power of each attribute is estimated. The attributes are sorted by an index of classification power, the original dataset is decomposed into subtables. Clustering, rule induction and table decomposition methods are applied to the subtables in a recursive way. The method was applied to datasets stored in hospital information system stored in 10 years. The results show that the reuse of stored data will give a powerful tool for construction of clinical process, which can be viewed as data-oriented management of nursing schedule.

This paper proposes maintenance and construction of clinical pathway by using data on histories of nursing orders. The methods consist of three process. First, data on counting sum of executed orders are extracted from hospital informaton system. Then, clustering is applied to the data and the labels for grouping are obtained. By using the labels, rule induction is applied, and classification power of each attribute is estimated. The attributes are sorted by an index of classification power, the original table is decomposed into sub tables. Clustering, rule induction and table decomposition methods are applied to the sub tables in a recursive way. The method was applied to a dataset whose patients had an operation of cataract a. The results show that the reuse of stored data will give a powerful tool for maintenance of clinical pathway, which can be viewed as data-oriented management of nursing schedule.

Since hospital data include temporal trends of clinical symptoms and medical services, we can discover not only knowledge about temporal evolution of disease, but also one about medical practice from hospital information system. This paper proposes temporal data mining process and applied the method to capture temporal knowledge about nursing practice. The results show that the reuse of stored data will give a powerful tool for management of nursing schedule and lead to improvement of hospital services.

Since hospital data include temporal trends of clinical symptoms and medical services, we can discover not only knowledge about temporal evolution of disease, but also one about medical practice from hospital information system, which will lead to data-oriented hospital management. This paper proposes temporal data mining process and applied the method to construction and revision of clinincal pathway by using temporal knowledge obtained from data. The results show that the reuse of stored data will give a powerful tool for maintenance and construction of clinincal pathway, which can be viewed as dataorientd management of nursing schedule.